Faculty of Science, Department of Earth and Planetary Science, Kobe University, Nada, Kobe 657-8501, Japan.
J R Soc Interface. 2013 Jun 26;10(86):20130486. doi: 10.1098/rsif.2013.0486. Print 2013 Sep 6.
In reports addressing animal foraging strategies, it has been stated that Lévy-like algorithms represent an optimal search strategy in an unknown environment, because of their super-diffusion properties and power-law-distributed step lengths. Here, starting with a simple random walk algorithm, which offers the agent a randomly determined direction at each time step with a fixed move length, we investigated how flexible exploration is achieved if an agent alters its randomly determined next step forward and the rule that controls its random movement based on its own directional moving experiences. We showed that our algorithm led to an effective food-searching performance compared with a simple random walk algorithm and exhibited super-diffusion properties, despite the uniform step lengths. Moreover, our algorithm exhibited a power-law distribution independent of uniform step lengths.
在涉及动物觅食策略的报告中，已经指出，由于 Lévy 算法具有超级扩散特性和幂律分布的步长，因此它代表了未知环境中的最佳搜索策略。在这里，我们从一个简单的随机游走算法开始，该算法在每个时间步为代理提供一个随机确定的方向，移动长度固定，然后我们研究了如果代理根据自己的方向移动经验改变其随机确定的下一步以及控制其随机移动的规则，如何灵活地进行探索。我们表明，与简单的随机游走算法相比，我们的算法在有效地进行食物搜索方面表现出色，并且尽管步长均匀，但它仍具有超级扩散特性。此外，我们的算法表现出幂律分布，与均匀步长无关。
